diff --git a/R/riskDecomp.R b/R/riskDecomp.R
index 62cc9815..65ad887f 100644
--- a/R/riskDecomp.R
+++ b/R/riskDecomp.R
@@ -78,12 +78,17 @@
 #' wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)  
 #'                                                      
 #' # fit a fundamental factor model
+#' exposure.vars = c("SECTOR","ROE","BP","PM12M1M","SIZE", "ANNVOL1M", "EP")
 #' fit.cross <- fitFfm(data = dat, 
-#'               exposure.vars = c("SECTOR","ROE","BP","MOM121","SIZE","VOL121",
-#'               "EP"),date.var = "DATE", ret.var = "RETURN", asset.var = "TICKER", 
-#'               fit.method="WLS", z.score = "crossSection")
+#'               exposure.vars = exposure.vars,
+#'               date.var = "DATE", 
+#'               ret.var = "RETURN", 
+#'               asset.var = "TICKER", 
+#'               fit.method="WLS", 
+#'               z.score = "crossSection")
 #'               
 #' decompES = riskDecomp(fit.cross, risk = "ES") 
+#' 
 #' #get the factor contributions of risk 
 #' portES.decomp = riskDecomp(fit.cross, weights = wtsStocks145GmvLo, risk = "ES", portDecomp = TRUE)  
 #' @export
diff --git a/R/summary.pafm.r b/R/summary.pafm.r
index 4b576a72..6a9c9d77 100644
--- a/R/summary.pafm.r
+++ b/R/summary.pafm.r
@@ -12,10 +12,11 @@
 #' @examples
 #' # load data from the database
 #' data(managers, package = 'PerformanceAnalytics')
+#' 
 #' # fit the factor model with LS
-#' fit.ts <- fitTsfm(asset.names=colnames(managers[,(1:6)]), 
-#'                   factor.names=c("EDHEC.LS.EQ","SP500.TR"),
-#'                   data=managers)
+#' fit.ts <- fitTsfm(asset.names = colnames(managers[,(1:6)]), 
+#'                   factor.names = c("EDHEC LS EQ","SP500 TR"),
+#'                   data = managers)
 #'   
 #' fm.attr <- paFm(fit.ts)
 #' summary(fm.attr)
diff --git a/R/summary.tsfm.r b/R/summary.tsfm.r
index 1c716d46..1124efcb 100644
--- a/R/summary.tsfm.r
+++ b/R/summary.tsfm.r
@@ -45,10 +45,6 @@
 #' @examples
 #'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
-#' colnames(managers)
-#'  # Make syntactically valid column names
-#' colnames(managers) <- make.names( colnames(managers))
-#' colnames(managers)
 #' 
 #' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
 #'                factor.names=colnames(managers[,7:9]), 
diff --git a/R/summary.tsfmUpDn.r b/R/summary.tsfmUpDn.r
index 57caa8b5..695790e7 100644
--- a/R/summary.tsfmUpDn.r
+++ b/R/summary.tsfmUpDn.r
@@ -34,16 +34,12 @@
 #' @examples
 #'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
-#' colnames(managers)
-#'  # Make syntactically valid column names
-#' colnames(managers) <- make.names( colnames(managers))
-#' colnames(managers)
 #' 
 #' # example: Up and down market factor model with LS fit
-#' fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),
-#'                        mkt.name="SP500.TR",
-#'                        data=managers, 
-#'                        fit.method="LS")
+#' fitUpDn <- fitTsfmUpDn(asset.names = colnames(managers[,(1:6)]),
+#'                        mkt.name = "SP500 TR",
+#'                        data = managers, 
+#'                        fit.method = "LS")
 #'  
 #'  summary(fitUpDn)
 #' 
diff --git a/man/riskDecomp.Rd b/man/riskDecomp.Rd
index 67a8bc38..2c5ee0e6 100644
--- a/man/riskDecomp.Rd
+++ b/man/riskDecomp.Rd
@@ -108,12 +108,17 @@ data("wtsStocks145GmvLo")
 wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)  
                                                      
 # fit a fundamental factor model
+exposure.vars = c("SECTOR","ROE","BP","PM12M1M","SIZE", "ANNVOL1M", "EP")
 fit.cross <- fitFfm(data = dat, 
-              exposure.vars = c("SECTOR","ROE","BP","MOM121","SIZE","VOL121",
-              "EP"),date.var = "DATE", ret.var = "RETURN", asset.var = "TICKER", 
-              fit.method="WLS", z.score = "crossSection")
+              exposure.vars = exposure.vars,
+              date.var = "DATE", 
+              ret.var = "RETURN", 
+              asset.var = "TICKER", 
+              fit.method="WLS", 
+              z.score = "crossSection")
               
 decompES = riskDecomp(fit.cross, risk = "ES") 
+
 #get the factor contributions of risk 
 portES.decomp = riskDecomp(fit.cross, weights = wtsStocks145GmvLo, risk = "ES", portDecomp = TRUE)  
 }
diff --git a/man/summary.pafm.Rd b/man/summary.pafm.Rd
index b8099650..b6c5c566 100644
--- a/man/summary.pafm.Rd
+++ b/man/summary.pafm.Rd
@@ -20,10 +20,11 @@ Generic function of summary method for \code{paFm}.
 \examples{
 # load data from the database
 data(managers, package = 'PerformanceAnalytics')
+
 # fit the factor model with LS
-fit.ts <- fitTsfm(asset.names=colnames(managers[,(1:6)]), 
-                  factor.names=c("EDHEC.LS.EQ","SP500.TR"),
-                  data=managers)
+fit.ts <- fitTsfm(asset.names = colnames(managers[,(1:6)]), 
+                  factor.names = c("EDHEC LS EQ","SP500 TR"),
+                  data = managers)
   
 fm.attr <- paFm(fit.ts)
 summary(fm.attr)
diff --git a/man/summary.tsfm.Rd b/man/summary.tsfm.Rd
index 906c4abd..42ae4d6b 100644
--- a/man/summary.tsfm.Rd
+++ b/man/summary.tsfm.Rd
@@ -59,10 +59,6 @@ predictions.
 \examples{
  # load data
 data(managers, package = 'PerformanceAnalytics')
-colnames(managers)
- # Make syntactically valid column names
-colnames(managers) <- make.names( colnames(managers))
-colnames(managers)
 
 fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
                factor.names=colnames(managers[,7:9]), 
diff --git a/man/summary.tsfmUpDn.Rd b/man/summary.tsfmUpDn.Rd
index 025e28ce..54d695da 100644
--- a/man/summary.tsfmUpDn.Rd
+++ b/man/summary.tsfmUpDn.Rd
@@ -45,16 +45,12 @@ objects and combines the coefficients interested together.
 \examples{
  # load data
 data(managers, package = 'PerformanceAnalytics')
-colnames(managers)
- # Make syntactically valid column names
-colnames(managers) <- make.names( colnames(managers))
-colnames(managers)
 
 # example: Up and down market factor model with LS fit
-fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),
-                       mkt.name="SP500.TR",
-                       data=managers, 
-                       fit.method="LS")
+fitUpDn <- fitTsfmUpDn(asset.names = colnames(managers[,(1:6)]),
+                       mkt.name = "SP500 TR",
+                       data = managers, 
+                       fit.method = "LS")
  
  summary(fitUpDn)